He’s a household name. And we’re thrilled to announce that he is going to be a keynote speaker at Domopalooza next month.

Billy Beane, the baseball general manager whose story was the subject of Michael Lewis’ bestselling book Moneyball: The Art of Winning an Unfair Game and who was portrayed by Brad Pitt in the film adaptation, is no stranger to algorithms.

Facing one of the smallest budgets for player salaries of any team in baseball in 2002, the Oakland A’s were in a bind. Beane, the team’s general manager, was fed up with his inability to outbid other teams for good players. He reached out to Paul DePodesta, a Harvard alum with a background in economics who had a knack for baseball statistics. The two of them used Bill James-style advanced statistics to take a second look at how the team was scouting talent.

Beane and DePodesta set about mining decades of data on hundreds of individual players in order to figure out the best strategy for recruiting good players. Their analysis revealed that baseball scouts were overlooking statistics that could accurately predict how many runs a player would score. In short, scouts were clueless when it came to accurately valuing talent.

Drawing from these conclusions, Beane realized that players who scored high on these overlooked statistics were probably undervalued by the bidding market. He began seeking out these “bargain” players, or players who were flying under the radar of other teams but whose statistics suggested that they would score runs.

Despite pushback from baseball scouts, Beane pulled the trigger on his radical new strategy for acquiring players. Beane bet big time on analytics and his efforts paid off. The A’s started to win, even against baseball teams that had much larger budgets. The team became the first team in over 100 years of American League baseball to win 20 consecutive games.

The Billy Beane story is one of the best-known data analytics case studies. Since the stodgy MLB machine woke up to the power of statistics, the science of player evaluation and recruiting has changed drastically. For instance, in-game data analysis has yielded insights about baseball pitchers and their tendencies to throw certain pitches in certain situations.

In the decade that has passed since the A’s legendary season, sports teams have been integrating statistical analysis into the way they play.

We’ve written previously about how big data insights shaped the 2014 World Cup in Brazil last year. Most notably, FIFA employed goal-line technology to determine whether a ball had passed the goal plane, allowing teams to settle disputed goals more fairly. In addition, many soccer teams used heat maps to analyze the movement of players on the field. Beane has been credited with helping to pioneer the entire field of sports analytics.

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“The one constant in the future of sports will be the game that is played between the lines,” wrote Beane in a recent WSJ op-ed. “Baseball, in particular, embraces historical continuity. But what drives the game – those who play it, how their play is evaluated, and those who make the evaluations – will fundamentally change.”

Beane envisions a world in which sports are no longer an exclusive club of insiders. Technology, he argues, is driving sports towards greater diversity and increased access.

“Technology will transform the social fabric of sport,” Beane writes.

As a cultural icon, Billy Beane may be the most famous general manager in sports history. Not only did he beat the system using statistical analysis, but he helped an entire industry reevaluate the way it was making decisions about how to win big.